A systematic examination of the gain- and loss-framed content of educational resources aimed at preventing doping among adolescent athletes
Bibliographic record
Abstract
Doping is a worldwide problem with researchers reporting a prevalence of doping that ranges between 6% and 34% of elite athletes. Primary doping prevention initiatives exist, and their effectiveness may be enhanced with the use of framed messages. Given that abstaining from using performance-enhancing substances is a low risk behavior with relatively certain outcomes, researchers suggest that gain-framed messages might have an advantage in promoting this behaviour; however, the degree to which prevention messages include gain- and loss-framed messages is unknown. The purpose of this study was to systematically identify and evaluate available educational health messages aimed at preventing doping among adolescent athletes to determine the degree to which they include gain- and loss-framed content. We systematically searched the internet through Google, Yahoo, Bing, and specific accredited sport and doping-prevention agencies for doping-prevention resources such as brochures, posters, and videos, in print or online. Our search yielded 60 resources which were reviewed by two separate members of the research team for their loss-framed, gain-framed, and non-framed content. The vast majority of the content (88.40%) was non-framed and the remainder was primarily loss-framed (11.37%). The resources included almost no gain-framed content (0.23%) despite suggestions that gain-framed messages may be effective in this context. Our findings suggest a need to test gain-framed messages as an alternative to the traditional loss-framed doping prevention messages as a means to enhance the efficacy of doping prevention initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".